I'm trying to fit a nonlinear model with nearly 50 variables (since there are year fixed effects). The problem is I have so many variables that I cannot write the complete formula down like
nl_exp = as.formula(y ~ t1*year.matrix[,1] + t2*year.matrix[,2]
+... +t45*year.matirx[,45] + g*(x^d))
nl_model = gnls(nl_exp, start=list(t=0.5, g=0.01, d=0.1))
where y
is the binary response variable, year.matirx
is a matrix of 45 columns (indicating 45 different years) and x
is the independent variable. The parameters need to be estimated are t1, t2, ..., t45, g, d
.
I have good starting values for t1, ..., t45, g, d
. But I don't want to write a long formula for this nonlinear regression.
I know that if the model is linear, the expression can be simplified using
l_model = lm(y ~ factor(year) + ...)
- I tried
factor(year)
ingnls
function but it does not work. Besides, I also tried
nl_exp2 = as.formula(y ~ t*year.matrix + g*(x^d))
nl_model2 = gnls(nl_exp2, start=list(t=rep(0.2, 45), g=0.01, d=0.1))
It also returns me error message.
So, is there any easy way to write down the nonlinear formula and the starting values in R
?